packages = c('tidyverse', 'lubridate',
'tidyquant', 'ggHoriPlot',
'timetk', 'ggthemes',
'plotly')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}
Data Preparation
Importing Data
The code chunk below is used to import companySG.csv into R.
company <- read_csv("./In-class_Ex10/data/companySG.csv")
Data wrangling
tidyquant integrates resources for collecting and analysing financial data with the tidy data infrastructure of the tidyverse, allowing for seamless interaction between each.
In this section, you will learn how to extract the daily stock values of a selected stocks from Yahoo Finance by using tidyquant.
Step 1: Pre-processing
We will import a pre-prepared company list called companySG.csv onto R. The list consists of top 45 companies by market capitalisation in Singapore. However, we just want the top 40.
Top40 <- company %>%
slice_max(`marketcap`, n=40) %>%
select(symbol)
Step 2: Get specific stock
tq_get() method will be used to extract daily values of these stocks from Yahoo Finance via APIs. The time period for the data was set from 1st January 2020 to 31st March 2021. The data are specified to be returned in daily intervals.
Stock40_daily <- Top40 %>%
tq_get(get = "stock.prices",
from = "2020-01-01",
to = "2022-03-31") %>%
group_by(symbol) %>%
tq_transmute(select = NULL,
mutate_fun = to.period,
period = "days")
Plotting a horizon graph
In this section, you will learn how to plot a horizon graph by using geom_horizon() of ggHoriPlot package.
Stock40_daily %>%
ggplot() +
geom_horizon(aes(x = date, y=adjusted), origin = "midpoint", horizonscale = 6)+
facet_grid(symbol~.)+
theme_few() +
scale_fill_hcl(palette = 'RdBu') +
theme(panel.spacing.y=unit(0, "lines"), strip.text.y = element_text(
size = 5, angle = 0, hjust = 0),
legend.position = 'none',
axis.text.y = element_blank(),
axis.text.x = element_text(size=7),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.ticks.y = element_blank(),
panel.border = element_blank()
) +
scale_x_date(expand=c(0,0), date_breaks = "1 month", date_labels = "%b%y") +
ggtitle('Daily Adjusted Prices (Jan 2020 to Mar 2022)')
Horizon graph makeover
- Instead of showing stock code, the stock name will be displayed.
- Adding reference lines
Step 1: Left join company data
left_join() of dplyr package is used to append fields from company data.frame onto Stock_daily data.frame. Next select() is used to select columns 1 to 8 and 11 to 12.
Stock40_daily <- Stock40_daily %>%
left_join(company) %>%
select(1:8, 11:12)
Step 2: Add the vertical reference lines
geom_vline() is used to add the vertical reference lines.
Stock40_daily %>%
ggplot() +
geom_horizon(aes(x = date, y=adjusted), origin = "midpoint", horizonscale = 6)+
facet_grid(Name~.)+
geom_vline(xintercept = as.Date("2020-03-11"), colour = "grey15", linetype = "dashed", size = 0.5)+
geom_vline(xintercept = as.Date("2020-12-14"), colour = "grey15", linetype = "dashed", size = 0.5)+
theme_few() +
scale_fill_hcl(palette = 'RdBu') +
theme(panel.spacing.y=unit(0, "lines"),
strip.text.y = element_text(size = 5, angle = 0, hjust = 0),
legend.position = 'none',
axis.text.y = element_blank(),
axis.text.x = element_text(size=7),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.ticks.y = element_blank(),
panel.border = element_blank()
) +
scale_x_date(expand=c(0,0), date_breaks = "1 month", date_labels = "%b%y") +
ggtitle('Daily Adjusted Prices (Jan 2020 to Mar 2022)')
Plotting Stock Price Line Graph: ggplot methods
In the code chunk below, geom_line() of ggplot2 is used to plot the stock prices.
Stock40_daily %>%
filter(symbol == "DBSDF") %>%
ggplot(aes(x = date, y = close)) +
geom_line() +
labs(title = "DBS Group Holdings Ltd (DBSDF) Daily Stock Price",
y = "Closing Price", x = "") +
theme_tq()
Plotting interactive stock price line graphs
In this section, we will create interactive line graphs for four selected stocks.
Step 1: Selecting the four stocks of interest.
Step 2: Plotting the line graphs by using ggplot2 functions and ggplotly() of plotly R package
p <- ggplot(selected_stocks, aes(x = date, y = adjusted))+
scale_y_continuous() +
geom_line() +
facet_wrap(~Name, scales = "free_y",) +
theme_tq() +
labs(title = "Daily stock prices of selected weak stocks", x = "", y = "Adjusted Price") +
theme(axis.text.x = element_text(size = 6), axis.text.y = element_text(size = 6))
ggplotly(p)
Plotting Candlestick Chart: tidyquant method
In this section, you will learn how to plot candlestick chart by using geom_candlestick() of tidyquant package.
Before plotting the candlesticks, the code chunk below will be used to define the end data parameter. It will be used when setting date limits throughout the examples.
end <- as_date("2022-03-31")
Now we are ready to plot the candlesticks by using the code chunk below.
Stock40_daily %>%
filter(symbol == "DBSDF") %>%
ggplot(aes(
x = date, y = close)) +
geom_candlestick(aes(
open = open, high = high,
low = low, close = close)) +
geom_line(size = 0.5)+
coord_x_date(xlim = c(end - weeks(12),
end),
ylim = c(20, 35),
expand = TRUE) +
labs(title = "DBS Group Holdings Ltd (DBSDF) Daily Stock Price",
y = "Closing Price", x = "") +
theme_tq()
Plotting candlestick chart and MA lines: tidyquant method
Things to learn from the code chunk:
- geom_MA is used to add the moving average line. It is a wrapper function of SMA() from the TTR package.
Stock40_daily %>%
filter(symbol == "DBSDF") %>%
ggplot(aes(
x = date, y = close)) +
geom_candlestick(aes(
open = open, high = high,
low = low, close = close)) +
geom_line(size = 0.5)+
geom_ma(color = "darkgreen", n = 20) +
geom_ma(color = "lightgreen", n = 20) +
coord_x_date(xlim = c(end - weeks(12),
end),
ylim = c(20, 35),
expand = TRUE) +
labs(title = "DBS Group Holdings Ltd (DBSDF) Daily Stock Price",
subtitle = "darkgreen = 1-day MA, lightgreen = 5-day MA",
y = "Closing Price", x = "") +
theme_tq()
Plotting Bollinger Bands: tidyquant method
In this section, you will learn how to plot bollinger bands by using geom_bbands() of tidyquant package.
Things you can learn from the code chunk:
- geom_bbands() plots a range around a moving average typically two standard deviations up and down. The moving average functions used are specified in SMA() from the TTR package.
Stock40_daily %>%
filter(symbol == "DBSDF") %>%
ggplot(aes(x=date, y=close))+
geom_line(size=0.5)+
geom_bbands(aes(
high = high, low = low, close = close),
ma_fun = SMA, sd = 2, n = 20,
size = 0.75, color_ma = "royalblue4",
color_bands = "red1")+
coord_x_date(xlim = c("2020-02-01",
"2022-03-31"),
expand = TRUE)+
labs(title = "DBS Group Holdings Ltd (DBSDF) Daily Stock Price",
subtitle = "dotted red lines = bollinger bands",
x = "Date", y ="Price") +
theme(legend.position="none")
Plotting Interactive Candlesticks Chart: ggplot2 and plotly R method
First, a candleStick_plot function is written as follows:
Credit: I learned this trick from RObservations #12: Making a Candlestick plot with the ggplot2 and tidyquant packages
candleStick_plot<-function(symbol, from, to){
tq_get(symbol, from = from, to = to, warnings = FALSE) %>%
mutate(greenRed=ifelse(open-close>0, "Red", "Green")) %>%
ggplot()+
geom_segment(aes(x = date, xend=date, y =open, yend =close, colour=greenRed), size=3)+
theme_tq()+
geom_segment(aes(x = date, xend=date, y =high, yend =low, colour=greenRed))+
scale_color_manual(values=c("ForestGreen","Red"))+
ggtitle(paste0(symbol," (",from," - ",to,")"))+
theme(legend.position ="none",
axis.title.y = element_blank(),
axis.title.x=element_blank(),
axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=1),
plot.title= element_text(hjust=0.5))
}
Plotting interactive candlestick chart: ggplot2 and plotly R method
p <- candleStick_plot("DBSDF",
from = '2022-01-01',
to = today())
ggplotly(p)